Improving multiclass ILP by combining partial rules with winnow algorithm: results on classification of dopamine antagonist molecules

  • Authors:

  • Sukree Sinthupinyo;Cholwich Nattee;Masayuki Numao;Takashi Okada;Boonserm Kijsirikul

  • Affiliations:

  • Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;Center for Information & Media Studies, Kwansei Gakuin University;Department of Computer Engineering, Chulalongkorn University

  • Venue:
  • JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
  • Year:
  • 2003

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Abstract

In this paper, we propose an approach which can improve Inductive Logic Programming in multiclass problems. This approach is based on the idea that if a whole rule cannot be applied to an example, some partial matches of the rule can be useful. The most suitable class should be the class whose important partial matches cover the example more than those from other classes. Hence, the partial matches of the rule, called partial rules, are first extracted from the original rules. Then, we utilize the idea of Winnow algorithm to weigh each partial rule. Finally, the partial rules and the weights are combined and used to classify new examples. The weights of partial rules show another aspect of the knowledge which can be discovered from the data set. In the experiments, we apply our approach to a multiclass real-world problem, classification of dopamine antagonist molecules. The experimental results show that the proposed method gives the improvement over the original rules and yields 88.58% accuracy by running 10-fold cross validation.